Did you just blink, reading the headline? No data scientist in his right mind will confess that he is fake and taking an opportunity to squeeze the ignoramus enterprises he represents for consulting. It is true that out of the ten scattered professionals that are hired for consulting, about three of them do the real work i.e. being involved in hardcore machine learning, recommending the right systems and modeling. The rest, they are simply re branded as they write SQL queries and help the product manager to prepare the dashboard. Across US and Europe, there are challenges that make data science consulting cutting edge. The first challenge is to spot the fake one and employ only the right ones… provided you understand what it is all about.

Let’s eliminate the fake ones and focus on what the real heroes do- even though it isn’t the sexiest job of this century, but is the biggest in North America and gaining credence in the rest of the world very quickly.

Data consultancy has grown five times over the last 5 years

The activity empowers enterprises when real heroes are employed

As consultancy in the usage of data increases, the real experts focus on making their clients understand the mechanisms that make the business move forward. They essentially build up the analytical skills along with core competencies.

Data science consulting considers four points:

  • Briefing the management of the enterprise.
  • Strategy building
  • Development
  • Training the workforce

They offer skills that will empower and lead to a better operational business model. All these points protect the operations while data is generated and used. The real heroes will further break down the strategy on the parameters listed below:

  1. Get a grip of the problem areas in data generation.
  2. Involve the workforce and other stakeholders to understand.
  3. Ingest the flow of information.
  4. Build strategies to clean and shape it into meaningful data.
  5. Next process is to evaluate and with available data create an operational model.
  6. Once, it is in place results can be analyzed and insights followed.
  7. The last parameter is to take action and set it in motion.

The consultant continues to play a traditional role

He uses the 7 parameters diligently

If you realize the real aspects of data science consulting, the insights and turning them into a goldmine is important. The real hero will understand the insight available and know what to do with it. He will have a process in mind to execute it after the workforce is trained to use it. As a proper actionable step, he will focus on the development which will involve designing the core data product with the right tools. He can be an asset if he is able to customize the product or system to the advantage of the enterprise. The development process is critical to specific problems. That is why customization is important.

Get the workforce to become data literate

Involving the client’s employees is a must

After the hard work that is done at the strategy level, training is important. Unless the employees are trained to use the data generated, the entire exercise is a failure. Employees understand how the enterprise works. When they use the insights and processes developed by the consulting procedure they can improve the system eventually. It is well known that when the employees are contributing and providing real-time feedback, it allows the data science consulting teams to become more effective.

How to get the right professionals?

Fake ones do spoil the scene

The fake or rebranded analyst who disguises as a consultant will be handling SQL queries and probably be preparing numerous copies of data on excel sheets and make tableaus. Naturally, he would also be making smart dashboards and giving reports. With fresh data, he may provide analysis on an ad hoc basis and be called the wizard who knows it all. Could he be a startup guy who is eager to get a foothold in the data science consulting industry? Perhaps, yes. Unlike the real hero who will manage copious amounts of structured and unstructured data, the fake ones will just show off the coding skills or know Poisson distribution. But will she/he be able to build the right products? This is where you catch the fake who is merely a statistician. The real one will be toiling on the four pillars of strategy and redefine company goals. Yes, there is a definite mismatch between the demand and supply.

If you have been able to understand this blog, it could help you rebuild the entire operational business model.

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